U.S. patent application number 14/494018 was filed with the patent office on 2016-03-24 for decisions support for patients with diabetes.
The applicant listed for this patent is Animas Corporation. Invention is credited to Jorge CAPURRO, Thomas MCCANN, Thomas SCHAIBLE.
Application Number | 20160082187 14/494018 |
Document ID | / |
Family ID | 55524783 |
Filed Date | 2016-03-24 |
United States Patent
Application |
20160082187 |
Kind Code |
A1 |
SCHAIBLE; Thomas ; et
al. |
March 24, 2016 |
DECISIONS SUPPORT FOR PATIENTS WITH DIABETES
Abstract
A decision support system includes a measurement device
configured to continuously measure a physiological parameter of a
patient. An insulin delivery device provides insulin to the patient
per an initial basal profile and the parameter measurements. A
storage device holds historical data of insulin delivery to the
patient. A processor determines deviations of the delivery of
insulin from the basal profile for one or more time period(s) using
the historical data, computes a respective first basal-profile
adjustment for each of the one or more time period(s) using the
determined deviations, and annunciates the computed first
basal-profile adjustment(s). A method of recommending a basal-rate
adjustment includes measuring the parameter, infusing the patient
with insulin and storing the historical data, determining the
deviations from the basal profile, computing the first
basal-profile adjustments, and annunciating the computed first
basal-profile adjustment(s).
Inventors: |
SCHAIBLE; Thomas;
(Phoenixville, PA) ; MCCANN; Thomas; (Pottstown,
PA) ; CAPURRO; Jorge; (West Chester, PA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Animas Corporation |
West Chester |
PA |
US |
|
|
Family ID: |
55524783 |
Appl. No.: |
14/494018 |
Filed: |
September 23, 2014 |
Current U.S.
Class: |
604/504 ;
604/66 |
Current CPC
Class: |
A61M 2205/3569 20130101;
A61M 2205/3592 20130101; A61M 5/1723 20130101; A61M 2005/1726
20130101; A61M 2205/502 20130101; A61M 2205/52 20130101; A61B
5/14532 20130101; A61M 2205/3553 20130101; A61M 2230/201 20130101;
A61M 2205/3584 20130101; A61B 5/4839 20130101; A61M 5/14244
20130101 |
International
Class: |
A61M 5/172 20060101
A61M005/172; A61B 5/00 20060101 A61B005/00; A61B 5/145 20060101
A61B005/145; A61M 5/142 20060101 A61M005/142 |
Claims
1. A decision support system for a patient, the system comprising:
a) a measurement device configured to continuously measure a
physiological parameter of the patient; b) an insulin delivery
device configured to provide insulin to the patient according to an
initial basal profile and the continuous measurements of the
physiological parameter; c) a storage device holding historical
data of insulin delivery to the patient by the insulin delivery
device; and d) a processor coupled to the storage device, the
processor being configured to: i) determine deviations of the
delivery of insulin from the basal profile for one or more time
period(s) using the historical data; ii) compute a respective first
basal-profile adjustment for each of the one or more time period(s)
using the determined deviations; and iii) annunciate the computed
first basal-profile adjustment(s).
2. The system according to claim 1, wherein the one or more time
period(s) include a plurality of time periods and the processor is
further adapted to: a) determine whether or not at least one of the
plurality of time periods has deviations that are significantly
different than an overall deviation of the plurality of time
periods using a chi-squared (.chi..sup.2) test; and b) determine a
single first basal-profile adjustment for at least two different
ones of the plurality of time periods if the deviations for the at
least one of the plurality of time periods are not significantly
different than the overall deviation.
3. The system according to claim 1, wherein the processor is
further adapted to adjust the initial basal profile based upon the
computed first basal-profile adjustment(s).
4. The system according to claim 1, further including a display,
the processor configured to annunciate the computed first
basal-profile adjustment(s) by presenting a visual indication
thereof on the display.
5. The system according to claim 4, wherein each first
basal-profile adjustment includes a respective delivery rate and
the visual indication includes textual representation(s) of the
respective delivery rate(s).
6. The system according to claim 1, further including a user
interface adapted to receive input, the processor further adapted
to receive the historical data via the user interface and store the
received historical data in the storage device.
7. The system according to claim 1, wherein the historical data
includes bolus data and the processor is further configured to
filter meal data out of the historical data using the bolus
data.
8. The system according to claim 1, wherein the measurement device
comprises a continuous glucose monitor and the measured
physiological parameter comprises blood glucose.
9. The system according to claim 8, wherein the processor is
further configured to store blood glucose measurements of the
patient and filter meal data out of the historical data using the
stored measurements of the blood glucose.
10. The system according to claim 8, wherein the processor is
further configured to: a) store a plurality of the blood glucose
measurements; b) determine deviations of blood glucose level from a
stored aim range for one or more time period(s) using the stored
blood glucose measurements; c) compute a respective second
basal-profile adjustment for each of the one or more time period(s)
using the determined deviations; and d) annunciate the respective
second basal-profile adjustment(s).
11. The system according to claim 10, wherein the processor is
further configured to filter meal data out of the stored blood
glucose measurements using the historical data.
12. The system according to claim 10, wherein the processor is
configured to determine the deviations of blood glucose level by
determining the extent to which each of the stored blood glucose
measurements is outside the stored aim range, and determining that
the deviation for one of the time period(s) is zero if the stored
measurements during that time period are within the stored aim
range.
13. The system according to claim 8, wherein the processor is
further configured to: a) store a plurality of the blood glucose
measurements for a selected time period; b) select two stored
measurements using the historical data, the two stored measurements
corresponding to a glucose correction bolus during the selected
time period; c) determine a glucose effect of the glucose
correction bolus using the selected stored measurements and a
stored aim range; d) compute an adjustment to an insulin
sensitivity factor for the selected time period using the
determined glucose effect; and e) annunciate the computed
adjustment to the insulin sensitivity factor.
14. The system according to claim 8, wherein the storage device
holds an insulin-carbohydrate ratio and the processor is further
configured to: a) store a plurality of the blood glucose
measurements for a selected time period; b) select at least one
stored measurement using the historical data, the selected at least
one stored measurement corresponding to carbohydrate correction
boluses during the selected time period; c) determine a respective
deviation for each of the selected stored measurements with respect
to a stored aim range; d) compute an adjustment to the
insulin-carbohydrate ratio for the selected time period using the
determined deviations and the insulin-carbohydrate ratio; and e)
annunciate the respective adjustment to the insulin-carbohydrate
ratio.
15. The system according to claim 14, wherein the storage device
further holds a glucose-carbohydrate ratio and the processor is
further configured to compute the adjustment to the
insulin-carbohydrate ratio using the stored glucose-carbohydrate
ratio.
16. A method of recommending a basal-rate adjustment for an
insulin-delivery system, the method comprising: continuously
measuring a physiological parameter of a patient; repeatedly
infusing the patient with insulin according to an initial basal
profile and the continuous physiological parameter measurements;
storing historical data of the delivery of insulin; using a
processor, automatically determining deviations of the delivery of
insulin from the basal profile for one or more time period(s) using
the stored historical data; using the processor, automatically
computing a respective first basal-profile adjustment for each of
the time period(s) using the determined deviations; and using the
processor, automatically annunciating the computed first
basal-profile adjustment(s).
17. The method according to claim 16, wherein the physiological
parameter is blood glucose.
18. The method according to claim 17, further including
automatically performing the following using the processor: storing
a plurality of the blood glucose measurements; determining
deviations of blood glucose level from a stored aim range for one
or more time period(s) using the stored measurements; computing a
respective second basal-profile adjustment for each of the time
period(s) using the determined deviations; and annunciating the
computed second basal-profile adjustment(s).
19. The method according to claim 17, further including
automatically performing the following using the processor: storing
a plurality of the blood glucose measurements for a selected time
period; selecting two stored measurements using the historical
data, the two selected measurements corresponding to a glucose
correction bolus during the selected time period; determining a
glucose effect of the glucose correction bolus using the selected
stored measurements and a stored aim range; computing an adjustment
to an insulin sensitivity factor for the selected time period using
the determined glucose effect; and annunciating the computed
adjustment to the insulin sensitivity factor.
20. The method according to claim 17, further including
automatically performing the following using the processor: storing
a plurality of the blood glucose measurements for a selected time
period; selecting at least one of the stored measurements using the
historical data, the at least one selected measurement
corresponding to carbohydrate correction boluses during the
selected time period; determining a respective deviation for each
selected stored measurements with respect to a stored aim range;
computing an adjustment to the insulin-carbohydrate ratio for the
selected time period using the determined deviations and the
insulin-carbohydrate ratio; and annunciating the computed
adjustment to the insulin-carbohydrate ratio.
Description
TECHNICAL FIELD
[0001] This application relates generally to the field of
electronic systems for monitoring biological properties of a
patient's body, and more specifically to medical monitoring
systems.
BACKGROUND
[0002] Diabetes mellitus is a chronic metabolic disorder caused by
an inability of the pancreas to produce sufficient amounts of the
hormone insulin, resulting in the decreased ability of the body to
metabolize glucose. This failure leads to hyperglycemia, i.e. the
presence of an excessive amount of glucose in the blood plasma.
Persistent hyperglycemia and hypoinsulinemia have been associated
with a variety of serious symptoms and life-threatening long-term
complications such as dehydration, ketoacidosis, diabetic coma,
cardiovascular diseases, chronic renal failure, retinal damage and
nerve damages with the risk of amputation of extremities. Because
restoration of endogenous insulin production is not yet possible, a
permanent therapy is necessary which provides constant glycemic
control in order to always maintain the level of blood glucose (BG)
within normal limits. Such glycemic control is achieved by
regularly supplying external insulin to the body of the patient to
thereby reduce the elevated levels of blood glucose.
[0003] External biologic agents such as, for example, insulin or
its analogs, can be administered as multiple daily injections of a
mixture of rapid and intermediate-acting drugs via a hypodermic
syringe. Improved glycemic control can be achieved by the so-called
"intensive hormone" therapy which is based on multiple daily
injections, including one or two injections per day of a long
acting hormone for providing basal hormone and additional
injections of rapidly acting hormone before each meal in an amount
proportional to the size of the meal. Although traditional syringes
have at least partly been replaced by insulin pens, the frequent
injections are nevertheless very inconvenient for the patient,
particularly those who are incapable of reliably self-administering
injections. For some patients, substantial improvements in diabetes
therapy have been achieved by the development of drug delivery
devices, such as pumps and other insulin delivery or infusion
systems, that relieve the patient of the need for syringes or drug
pens and the need to administer multiple daily injections. Drug
delivery devices can be constructed as an implantable device for
subcutaneous arrangement or can be constructed as an external
device with an infusion set for subcutaneous infusion to the
patient via the transcutaneous insertion of a catheter, cannula or
a transdermal drug transport, such as through a patch.
[0004] Blood or interstitial glucose monitoring can be used to
achieve acceptable glycemic control. The determination of blood
glucose concentration can be performed by means of an episodic
measuring device, such as a hand-held electronic blood-glucose
meter, that receives blood samples on enzyme-based test strips and
calculates the blood glucose value based on an electrochemical
reaction of the blood and the enzyme. An example of a handheld
glucose meter/controller unit is the ONETOUCH PING.TM. from JOHNSON
& JOHNSON.RTM.. Continuous glucose monitoring (CGM) using a
sensor inserted into or implanted in the body can also be used. A
combination of a CGM and a drug delivery device can be used to
provide closed loop control of the insulin(s) being infused into
the diabetic patients. To allow for closed-loop control of the
infused insulins, proportional-integral-derivative ("PID")
controllers and model predictive controllers (MPC) have been used.
The term "continuous" includes unceasing monitoring as well as
frequent sampling. Exemplary CGM sensors generally sample glucose
on a regular time scale, e.g., once per five minutes. Closed-loop
control updates can be performed, e.g., in the time intervals
between glucose measurements.
[0005] Drug delivery devices generally provide insulin at a "basal
rate," i.e., provide a certain amount of insulin every few minutes
in a pre-programmed, daily pattern. Some drug delivery devices
permit the user to manually request that a "bolus," a specified
amount of insulin, be delivered at a specified time. For example,
before a meal, the user can request a bolus of additional insulin
be delivered to process the glucose produced by digestion of the
meal (a "carbohydrate correction bolus"). In another example,
during a hyperglycemic excursion from a target blood-glucose range,
the user can request a bolus to reduce blood sugar (a "glucose
correction bolus"). Correction bolus amounts can be determined
using an insulin-carbohydrate ratio ("I:C") for carbohydrate
correction boluses, and an insulin sensitivity factor ("ISF") for
glucose correction boluses. As used herein, the term "parameter"
(or "parameters") can refer to any or all of one or more basal
rate(s), I:C value(s), or ISF value(s).
[0006] Parameters are generally set by a "titration" process. A
patient's doctor selects initial values based on height, weight, or
other factors, together with tables of statistical data. The
patient then uses the pump and monitors blood glucose for a period
of time, e.g., two weeks to three months. At the end of the period,
the doctor reviews blood-glucose measurements and data on the
operation of the pump during the period and determines adjustments
to basal rates, I:C, or ISF. Adjustments can apply to an entire
daily cycle or only part of a day (e.g., morning or night-time). In
an example, if morning fasting glucose has consistently tested high
during the period, the doctor can increase the basal rate during
the overnight hours. This titration process is iterative and can be
very time-consuming. Moreover, over the course of a long period
such as three months, the patient's physiology can change, possibly
decreasing the quality of care provided by the selected parameters.
Moreover, the amount of data to be reviewed when the patient visits
the doctor for updated parameters can be significant, requiring the
doctor to spend considerable time reviewing the parameters.
[0007] As used herein, the term "dose period" or "scheduled dose
period" refers to a period of time over which doses of insulin or
other drugs, or the parameters used in determining the doses, are
constant (barring boluses or other user actions). The term "long
cycle" refers to a recurring pattern of dose periods. In an
example, the dose period is hourly and the long cycle is daily.
This example applies to an insulin delivery device that can deliver
a (possibly different) amount of basal insulin every hour of the
day, but the amount of basal insulin delivered, e.g., from 8 am to
9 am is the same each day. Such a device can store 24 basal insulin
dose rates (U/hr) in a memory. In another example, the dose period
is every three hours and the long cycle is 56 dose periods. This
provides a selected (possibly unique) basal dose for each
three-hour block throughout a week, after which the long cycle of
56 dose periods repeats. In still other examples, the dose period
is 15 minutes or five minutes. The term "infusion period" refers to
a period of time during which a selected amount of insulin is
infused. For example, if a dosage of 3 U is to be applied over a
one-hour dose period, the infusion period can be ten minutes and
0.5 U of insulin can be supplied to the patient in each of the six
infusion periods in the hour.
SUMMARY OF THE DISCLOSURE
[0008] In one embodiment, therefore, a decision support system for
a patient has been devised. The system may include the following
components: [0009] a) a measurement device configured to
continuously measure a physiological parameter of the patient;
[0010] b) an insulin delivery device configured to provide insulin
to the patient according to an initial basal profile and the
continuous measurements of the physiological parameter; [0011] c) a
storage device holding historical data of insulin delivery to the
patient by the insulin delivery device; and [0012] d) a processor
coupled to the storage device, the processor being configured to:
[0013] i) determine deviations of the delivery of insulin from the
basal profile for one or more time period(s) using the historical
data; [0014] ii) compute a respective first basal-profile
adjustment for each of the one or more time period(s) using the
determined deviations; and [0015] iii) annunciate the computed
first basal-profile adjustment(s).
[0016] In another embodiment, a method of recommending a basal-rate
adjustment for an insulin-delivery system is provided. The method
can be achieved by: [0017] continuously measuring a physiological
parameter of a patient; [0018] repeatedly infusing the patient with
insulin according to an initial basal profile and the continuous
physiological parameter measurements; [0019] storing historical
data of the delivery of insulin; [0020] using a processor,
automatically determining deviations of the delivery of insulin
from the basal profile for one or more time period(s) using the
stored historical data; [0021] using the processor, automatically
computing a respective first basal-profile adjustment for each of
the time period(s) using the determined deviations; and [0022]
using the processor, automatically annunciating the computed first
basal-profile adjustment(s).
[0023] Each of these embodiments, exemplary of the present
invention, can provide improved determination and recommendation of
adjustments to increase a patient's quality of care.
[0024] Accordingly, in any of the embodiments described earlier,
the following features may also be utilized in various combinations
with the previously disclosed embodiments. For example, the system
can include the processor configured to process data for a
plurality of time periods, to determine whether or not at least one
of the plurality of time periods has deviations that are
significantly different than an overall deviation of the plurality
of time periods using a chi-squared (.chi..sup.2) test; and to
determine a single first basal-profile adjustment for at least two
different ones of the plurality of time periods if the deviations
for the at least one of the plurality of time periods are not
significantly different than the overall deviation. The processor
can be further adapted to adjust the initial basal profile based
upon the computed first basal-profile adjustment(s). The system can
include a display and the processor can be configured to annunciate
the computed first basal-profile adjustment(s) by presenting a
visual indication thereof on the display. Each first basal-profile
adjustment can include a respective delivery rate and the visual
indication can include textual representation(s) of the respective
delivery rate(s). The system can include a user interface adapted
to receive input and the processor can be further adapted to
receive the historical data via the user interface and store the
received historical data in the storage device. The historical data
can include bolus data and the processor can be further configured
to filter meal data out of the historical data using the bolus
data. The measurement device can include a continuous glucose
monitor and the measured physiological parameter can include blood
glucose. The processor can be further configured to store blood
glucose measurements of the patient and filter meal data out of the
historical data using the stored measurements of the blood glucose.
The processor can be further configured to store a plurality of the
blood glucose measurements; determine deviations of blood glucose
level from a stored aim range for one or more time period(s) using
the stored blood glucose measurements; compute a respective second
basal-profile adjustment for each of the one or more time period(s)
using the determined deviations; and annunciate the respective
second basal-profile adjustment(s). The processor can be further
configured to filter meal data out of the stored blood glucose
measurements using the historical data. The processor can be
configured to determine the deviations of blood glucose level by
determining the extent to which each of the stored blood glucose
measurements is outside the stored aim range, and determining that
the deviation for one of the time period(s) is zero if the stored
measurements during that time period are within the stored aim
range. The processor can be further configured to store a plurality
of the blood glucose measurements for a selected time period;
select two stored measurements using the historical data, the two
stored measurements corresponding to a glucose correction bolus
during the selected time period; determine a glucose effect of the
glucose correction bolus using the selected stored measurements and
a stored aim range; compute an adjustment to an insulin sensitivity
factor for the selected time period using the determined glucose
effect; and annunciate the computed adjustment to the insulin
sensitivity factor. The storage device can hold an
insulin-carbohydrate ratio and the processor can be further
configured to store a plurality of the blood glucose measurements
for a selected time period; select at least one stored measurement
using the historical data, the selected at least one stored
measurement corresponding to carbohydrate correction boluses during
the selected time period; determine a respective deviation for each
of the selected stored measurements with respect to a stored aim
range; compute an adjustment to the insulin-carbohydrate ratio for
the selected time period using the determined deviations and the
insulin-carbohydrate ratio; and annunciate the respective
adjustment to the insulin-carbohydrate ratio. The storage device
can further hold a glucose-carbohydrate ratio and the processor can
be further configured to compute the adjustment to the
insulin-carbohydrate ratio using the stored glucose-carbohydrate
ratio.
[0025] In various examples, the method can include measuring blood
glucose as the physiological parameter. The method can include
automatically, using the processor, storing a plurality of the
blood glucose measurements; determining deviations of blood glucose
level from a stored aim range for one or more time period(s) using
the stored measurements; computing a respective second
basal-profile adjustment for each of the time period(s) using the
determined deviations; and annunciating the computed second
basal-profile adjustment(s). The method can include automatically,
using the processor, storing a plurality of the blood glucose
measurements for a selected time period; selecting two stored
measurements using the historical data, the two selected
measurements corresponding to a glucose correction bolus during the
selected time period; determining a glucose effect of the glucose
correction bolus using the selected stored measurements and a
stored aim range; computing an adjustment to an insulin sensitivity
factor for the selected time period using the determined glucose
effect; and annunciating the computed adjustment to the insulin
sensitivity factor. The method can include automatically, using the
processor, storing a plurality of the blood glucose measurements
for a selected time period; selecting at least one of the stored
measurements using the historical data, the at least one selected
measurement corresponding to carbohydrate correction boluses during
the selected time period; determining a respective deviation for
each selected stored measurements with respect to a stored aim
range; computing an adjustment to the insulin-carbohydrate ratio
for the selected time period using the determined deviations and
the insulin-carbohydrate ratio; and annunciating the computed
adjustment to the insulin-carbohydrate ratio.
[0026] In the aforementioned aspects of the disclosure, the steps
of measuring, infusing, storing, determining, computing,
annunciating, storing blood glucose measurements, determining
deviations of blood glucose level, computing second adjustments,
annunciating second adjustments, storing, selecting, determining
glucose effect, computing adjustment, annunciating adjustment,
storing, selecting, determining, computing, and annunciating may be
performed be an electronic circuit or a processor. These steps may
also be implemented as executable instructions stored on a computer
readable medium; the instructions, when executed by a computer may
perform the steps of any one of the aforementioned methods.
[0027] In additional aspects of the disclosure, there are computer
readable media, each medium comprising executable instructions,
which, when executed by a computer, perform the steps of any one of
the aforementioned methods.
[0028] In additional aspects of the disclosure, there are devices,
such as test meters or analyte testing devices, each device or
meter comprising an electronic circuit or processor configured to
perform the steps of any one of the aforementioned methods.
[0029] These and other embodiments, features and advantages will
become apparent to those skilled in the art when taken with
reference to the following more detailed description of various
exemplary embodiments of the invention in conjunction with the
accompanying drawings that are first briefly described.
BRIEF DESCRIPTION OF THE DRAWINGS
[0030] The accompanying drawings, which are incorporated herein and
constitute part of this specification, illustrate presently
preferred embodiments of the invention, and, together with the
general description given above and the detailed description given
below, serve to explain features of the invention. For the sake of
clarity, like reference numerals herein represent like
elements.
[0031] FIG. 1 illustrates an exemplary glucose-monitoring and
insulin delivery system and related components;
[0032] FIG. 2 shows an exemplary decision-support system for a
patient and related components; and
[0033] FIGS. 3A-3B are a flowchart illustrating exemplary methods
for recommending adjustments.
DETAILED DESCRIPTION
[0034] The following detailed description should be read with
reference to the drawings, in which like elements in different
drawings are identically numbered. The drawings, which are not
necessarily to scale, depict selected embodiments and are not
intended to limit the scope of the invention or the attached
claims.
[0035] As used herein, the terms "about" or "approximately" for any
numerical values or ranges indicate a suitable dimensional
tolerance that allows the part or collection of components to
function for its intended purpose as described herein. More
specifically, "about" or "approximately" may refer to the range of
values not at least .+-.10% of the recited value, e.g. "about 90%"
may refer to the range of values from 81% to 99%. Throughout this
disclosure, blood glucose values are given in mg/dL. Corresponding
values in mmol/L can be calculated and used in any aspect described
herein.
[0036] Throughout this disclosure, the terms "patient" and
"subject" are used interchangeably. These terms can refer to any
human or animal subject and are not intended to limit the systems
or methods to human use, although use of the subject invention in a
human patient represents a preferred embodiment. Furthermore, in
this disclosure, the term "user" can refer to a patient using a
glucose measuring or drug delivery device or to another person
(e.g., a parent or guardian, nursing staff member, home care
employee, or other caretaker) using such a device. The term
"healthcare provider" or "HCP" refers generally to doctors, nurses,
and individuals other than the patient that provide health care
services to the patient. The term "drug" may include hormones,
biologically active materials, pharmaceuticals or other chemicals
that cause a biological response (e.g., a glycemic response) in the
body of a user or patient.
[0037] FIG. 1 illustrates an exemplary glucose-monitoring and
insulin delivery system 100, e.g., an artificial pancreas. In this
specific example, an insulin delivery device 102 is connected to an
infusion set 106 via flexible tubing 108 and is controlled, e.g.,
by a controller 104. Various embodiments of the invention can also
be used with injections via syringe or insulin pen instead of or in
addition to infusion via the insulin delivery device 102. The
controller 104 or insulin delivery device 102 can communicate with
a continuous glucose monitoring (CGM) sensor 112. In an example,
the controller 104, insulin delivery device 102, and CGM sensor 112
cooperate to attempt to maintain a user's blood glucose level
within an aim range, e.g., 70-130 mg/dL, and more specifically to
try to drive the user's blood glucose level to a target, e.g., 100
mg/dL.
[0038] The insulin delivery device 102 is configured to transmit
and receive data to and from the controller 104 by, for example, a
radio frequency (RF) communications link 111. In one embodiment,
the insulin delivery device 102 is an insulin infusion device and
the controller 104 is a hand-held portable controller. In such an
embodiment, data transmitted from the insulin delivery device 102
to the controller 104 may include information such as, for example,
insulin delivery data, blood glucose (BG) information, basal,
bolus, insulin to carbohydrates ratio or insulin sensitivity
factor. The controller 104 can be configured to include a
closed-loop controller that has been programmed to receive
continuous glucose readings from the CGM sensor 112 via a radio
frequency (RF) communications link 110. The CGM sensor 112 can
measure glucose levels of interstitial fluid in the body, determine
corresponding blood glucose levels, and provide the BG levels to
the controller 104. The CGM sensor 112 can also or alternatively
provide data representative of or proportional to blood-glucose
values directly to the insulin delivery device 102 via a radio
frequency (RF) communications link 113 or a wired connection such
as a Universal Serial Bus (USB) cable.
[0039] Data transmitted from the controller 104 to the insulin
delivery device 102 may include glucose test results and a food
database to allow the insulin delivery device 102 to calculate the
amount of insulin to be delivered by the insulin delivery device
102. Alternatively, the controller 104 may perform basal dosing or
bolus calculation and send the results of such calculations to the
insulin delivery device. A glucose meter 114 (here, an episodic
blood-glucose meter), alone or in conjunction with the CGM sensor
112, provides data to either or both of the controller 104 and
insulin delivery device 102, e.g., via a radio frequency (RF)
communications link 117. The glucose meter 114 can measure a fluid
sample placed on a test strip 115. The two hatched areas on the
test strip 115 graphically represent two electrodes, as is
discussed below. The glucose meter 114 can include a display or
other interface to present information, or can present information
only via the controller 104.
[0040] For purposes of this embodiment, the test strip 115 is
defined by a planar substrate over which are disposed the
electrodes (shown hatched; formed of, e.g., sputtered gold or
palladium) and corresponding electrical contact pads (not shown).
The electrodes can be disposed on opposing sides of a
sample-receiving chamber, above and below the sample-receiving
chamber, or in other configurations. The exemplary test strip 115
includes a working electrode formed by sputtering a palladium (Pd)
coating on a polyester substrate and a reference electrode formed
by sputtering gold (Au) on the polyester substrate. A dry reagent
layer can be used and can include a buffer and a mediator. Various
enzymes in the reagent layer or elsewhere in the sample-receiving
chamber can assist in transducing the analyte (e.g., glucose) in
the fluid sample (e.g., blood, interstitial fluid, or control
solution) into a current, potential, or other quantity that can be
measured electrically. Exemplary enzymes include glucose oxidase,
glucose dehydrogenase (GDH) based on a pyrroloquinoline quinone
co-factor, and GDH based on a nicotinamide adenine dinucleotide
co-factor. Exemplary glucose sensors and associated components are
shown and described in U.S. Pat. Nos. 6,179,979, 8,163,162, and
6,444,115, which are incorporated by reference herein in their
entireties.
[0041] The controller 104 can present information and receive
commands via a touchscreen 144 or other devices discussed below
with reference to a user interface 230, FIG. 2. In the example
shown, the controller 104 is presenting tape and numeric indicators
representing a recent blood-glucose measurement ("120 mg/dL"). An
exemplary tape indicator 145 has (from top to bottom) yellow,
green, yellow, and red sections indicating various ranges of blood
glucose, and a pointer representing the recent measurement. The
measurement is in the green range so is colored green. The
controller 104 is also presenting a "BOLUS" soft key 146 on the
touchscreen 144. The user can press this soft key to request a
bolus of insulin.
[0042] The controller 104, the insulin delivery device 102, and the
CGM sensor 112 can be integrated into multi-function units in any
combination. For example, the controller 104 can be integrated with
the insulin delivery device 102 to form a combined device with a
single housing. Infusion, sensing, and controlling functions can
also be integrated into a monolithic artificial pancreas. In
various embodiments, the controller 104 is combined with the
glucose meter 114 into an integrated monolithic device having a
housing 130. Such an integrated monolithic device can receive a
test strip 125. In other embodiments, the controller 104 and the
glucose meter 114 are two separable devices that are dockable with
each other to form an integrated device. Each of the devices 102,
104, and 114 can include a suitable processor or microcontroller
(not shown for brevity) programmed to carry out various
functionalities. Examples of microcontrollers that can be used are
discussed below with reference to a processor 286, FIG. 2.
[0043] The insulin delivery device 102 or the controller 104 can
also be configured for bi-directional communication with a network
116 through, for example, a radio frequency communications link
118. One or more server(s) 126 or storage device(s) 128 can be
communicatively connected to the controller 104 via the network
116. In an example, the insulin delivery device 102 communicates
with a personal computer (e.g., the controller 104) via BLUETOOTH.
The controller 104 and the network 116 can be configured for
bi-directional wired communication through, for example, a
telephone land based communication network. The controller 104 can
include a smartphone, electronic tablet, or personal computer.
[0044] The insulin delivery device 102 can include any or all of:
electronic signal processing components including a central
processing unit and memory elements for storing control programs
and operation data, a radio frequency module (not shown) for
sending and receiving communication signals (e.g., messages) to and
from the controller 104, a display for providing operational
information to the user, a plurality of navigational buttons for
the user to input information, a battery for providing power to the
system, an alarm (e.g., visual, auditory or tactile) for providing
feedback to the user, a vibrator for providing feedback to the
user, and an insulin delivery mechanism (e.g., a drug pump and
drive mechanism) for forcing a insulin from a insulin reservoir
(e.g., a insulin cartridge) through a side port connected via the
flexible tubing 108 to an infusion set 106 and into the body of the
user.
[0045] Various glucose management systems include an episodic
glucose sensor (e.g., the glucose meter 114) and an infusion pump.
An example of such a system is the ONETOUCH PING Glucose Management
System manufactured by the Animas Corporation. The "ezBG" feature
of this system computes an amount of insulin to be delivered by the
infusion pump using the results of an episodic glucose measurement.
Another example of a glucose management system is the ANIMAS
VIBE.TM. insulin pump, which communicates with a DEXCOM G4.TM. CGM
system manufactured by the DexCom Corporation. Interfaces can be
provided to connect these components. Closed-loop control
algorithms can be programmed in, e.g., the MATLAB.TM. language to
regulate the rate of insulin delivery based on the glucose level of
the patient, historical glucose measurement and anticipated future
glucose trends, and patient specific information.
[0046] FIG. 2 shows an exemplary decision support system for a
patient, including data-processing components for analyzing data
and performing other analyses and functions described herein, and
related components. A patient 1138 and a network 116 are not part
of the system but are shown for purposes of context. The controller
104 can communicate with a measurement device 200 (e.g., a CGM
sensor 112, FIG. 1) or the insulin delivery device 102, e.g., via a
peripheral system 220. The controller 104 can also communicate with
the network 116, e.g., a cellular telephone data network or the
Internet. The controller 104 can also include the user interface
230 and a storage device 240 communicatively connected to the
processor 286, as discussed below. The processor 286, upon receipt
of data from a device in the peripheral system 220, can store that
data in the storage device 240.
[0047] The measurement device 200 is configured to continuously
measure a physiological parameter of the patient. In an example,
the measurement device 200 comprises a continuous glucose monitor
and the measured physiological parameter comprises blood glucose.
The processor 286 in the controller 104 can receive glucose data
from the measurement device 200 (the CGM sensor 112, or the glucose
meter 114 using the test strip 115) and provide control signals to
the insulin delivery device 102 to deliver insulin to the patient
1138, as discussed below. The insulin delivery device 102 can also
or alternatively receive the glucose data from the measurement
device 200 and adjust the insulin to be delivered to the patient
1138.
[0048] In various aspects, the insulin delivery device 102 is
configured to provide insulin to the patient 1138 according to an
initial basal profile and the continuous measurements of the
physiological parameter. The initial basal profile can include data
of respective dosage(s) for one or more dose period(s). The dosage
can be, e.g., in units of insulin (U) per hour or per infusion.
[0049] The insulin delivery device 102 in this example receives the
continuous measurements of the physical parameter (e.g., glucose
data) from the measurement device 200 and operates a closed-loop
control law, e.g., using an embedded processor (not shown; e.g., a
processor similar to the processor 286) in the insulin delivery
device 102. In this way, the insulin delivery device can adjust for
at least some excursions in blood glucose. For example, during a
hyperglycemic excursion, the control law operates to increase the
amount of insulin delivered to the patient 1138 above the amount
specified in the initial basal profile. As used herein, the term
"force" refers to the difference between the amount of insulin
delivered in an infusion period or dose period and the amount of
insulin specified by the initial basal profile for that infusion
period or dose period. During a hyperglycemic excursion, the force
will generally be positive. During a hypoglycemic excursion, the
force will generally be negative.
[0050] According to this exemplary embodiment, the storage device
240 holds historical data of insulin delivery to the patient by the
insulin delivery device. The historical data can be received from
the insulin delivery device 102 via the peripheral system 220,
discussed below. In an example, the storage device 240 includes a
memory 241, e.g., a random-access memory, and a disk 242, e.g., a
tangible computer-readable storage device such as a hard drive or a
solid-state flash drive. The memory 241 or the disk 242 can store
data used by running programs. For example, the historical data of
insulin delivery can be stored in the memory 241 or on the disk
242.
[0051] The processor 286 can be coupled to the storage device 240
and configured to perform various functions described herein. For
example, the processor 286 can be configured to determine
deviations of the delivery of insulin from the basal profile for
one or more dose period(s) time period(s) in a day using the
historical data.
[0052] In an example, the historical data include U/hr values for
each dose period over the course of a long cycle. The processor 286
retrieves the historical data for a plurality of long cycles, e.g.,
about 30 long cycles, from the storage device 240. The processor
286 also retrieves the initial basal profile from the storage
device 240. For each dose period, the processor 286 computes, as a
deviation for that dose period, the average difference between the
historical dosage for that dose period and the initial basal
profile for that dose period.
[0053] In various embodiments, the historical data include n U/hr
values (n.gtoreq.1) for each five- or 15-minute interval (or other
interval length) in a dose period. These values represent the basal
rate recommended by the closed-loop algorithm. In various aspects,
if the amount of insulin delivered in a particular dose was
adjusted (e.g., limited) because of an algorithm to reduce the
probability of insulin-induced hypoglycemic excursions, the
historical data can include the adjusted amount, the pre-adjustment
amount, or the force (adjusted minus pre-adjustment). The forces,
i.e., the differences between each of the n values and the initial
basal rate for the dose period, are determined and divided by n to
determine the deviation.
[0054] In various examples, data are collected for 30 long cycles,
e.g., 30 days, or 14 long cycles or more, or at least 7 long
cycles. In various aspects, if the user requests recommendations
based on only seven days' historical data (or another selected
threshold on the number of long cycles), the processor 286 prompts
the user to confirm that those days are representative of the
patient's typical activity and not, e.g., a vacation week with
unusual mealtimes.
[0055] The processor 286 is further configured to compute a
respective first basal-profile adjustment for each of the dose
period(s) using the determined deviations. It should be noted that
the processor 286 can leave the doses unchanged for one or more
dose period(s), in various examples. In an example, the processor
286 determines that the respective first basal-profile adjustment
is zero if the deviation has a magnitude less than a selected
threshold, e.g., within .+-.0.5 U/hr. The processor 286 determines
that the adjustment is the deviation if the deviation has a
magnitude over the threshold, e.g., not within .+-.0.5 U/hr. The
threshold can be selected based on the granularity of dosing
provided by the insulin delivery device 102.
[0056] In at least one embodiment, the processor 286 is configured
to annunciate the computed first basal-profile adjustment(s). The
processor 286 can, e.g., present a human-perceptible indication of
the adjustment(s) via the user interface 230. This advantageously
provides additional information to doctors or patients and assists
doctors in concentrating on clinically-relevant deviations. To
further assist doctors, the processor 286 can present fewer than
all of the determined adjustment(s), e.g., by filtering out
adjustments having a magnitude lower than a doctor-selected or
other threshold.
[0057] The user interface 230 can include a display device, a
touchscreen, a processor-accessible memory, or any device or
combination of devices to which data is output by the processor
286. In this regard, if the user interface 230 includes a
processor-accessible memory, such memory can be part of the storage
device 240 even though the user interface 230 and the storage
device 240 are shown separately in FIG. 6. For example, the user
interface 230 can include one or more touchscreen(s), speaker(s),
buzzer(s), vibrator(s), button(s), switch(es), jack(s), plug(s), or
network connection(s).
[0058] In various aspects, the processor 286 is configured to
annunciate score(s) for one or more of the adjustment(s) instead of
or in addition to the numerical value(s) of those adjustment(s).
The processor 286 can be configured to determine each score using a
respective one of the adjustment(s). In an example, a score is
annunciated for each dose period. Annunciating scores rather than
adjustments advantageously permits healthcare providers to
concentrate on dose periods for which adjustments might, e.g.,
provide a therapeutic benefit.
[0059] In an example, the scores can be modeled on the academic
scoring system in use in the patient's country, e.g., A, B, C, D, F
(best to worst) in the United States or 1-7 in Scotland. Other
scoring systems can be used, e.g., the .largecircle., .DELTA., X
(best to worst) system used in Japan. In another example, colors,
shades of gray, or combinations thereof can be used, e.g., green,
yellow, red or white, gray, black (best to worst). The "worst"
score can represent the adjustment the processor 286 deems most
significant. The processor 286 can map between an adjustment and
the corresponding score in a linear or nonlinear fashion,
optionally with saturation and offset. For example, in an A-F
scale, the A, B, C, D, and F scores can cover respective ranges of
the adjustment arranged in a geometric progression, with A being
the widest band. In a specific example, with a ratio of 1.47 and
adjustments normalized from 0% (no adjustment) to 100% (a selected
adjustment-amount limit), A can be 0%-37%, B 37%-63%, C 63%-80%, D
80%-92%, and F 92%-100%. The A score can also be the narrowest
band. Scores can be presented in a chart of, e.g., the dose
periods.
[0060] In various aspects, the processor 286 is configured to
determine whether an adjustment should be made by applying a
statistical test to the determined deviations. In an exemplary
embodiment, the processor 286 retrieves the historical data dosage
values for a plurality of dose periods and determines respective
deltas by subtracting from each historical dosage for each dose
period the initial basal profile for that dose period. The
processor 286 then applies a chi-squared (.chi..sup.2) test to
determine whether the any one or more of the dose periods has
deltas that are significantly different from the deltas for the
others of the plurality of dose periods, e.g., at the 95%
confidence level or another selected confidence level. If the
deltas are significantly different for one or more of the dose
period(s), the processor 286 can compute and annunciate the first
basal-profile adjustments for the one or more of the dose
period(s).
[0061] In various aspects, therefore, the one or more time
period(s) include a plurality of time periods (e.g., dose periods).
The processor 286 is further adapted to determine whether or not at
least one of the plurality of time periods has deviations that are
significantly different than an overall deviation of the plurality
of time periods using a .chi..sup.2 test. This is discussed below.
The processor 286 is yet further configured to determine a single
first basal-profile adjustment for at least two different ones of
the plurality of time periods if the deviations for the at least
one of the plurality of time periods are not significantly
different than the overall deviation. The .chi..sup.2 test
indicates whether there are specific time periods, e.g., dose
periods, on which attention might profitably be focused. If there
is no such specific time period, the time periods as a whole may
still be in need of adjustment. This is also discussed below.
[0062] In an example, for each hour of the day or other dose
period, the processor 286 divides historical data values for a
plurality of infusion periods depending on whether the force during
that infusion period had a greater magnitude than a selected
threshold, e.g., a force more negative than -0.5 U/hr or more
positive than 0.5 U/hr was applied. For each dose period i, the
infusion periods with such a magnitude of force are counted as
O.sub.i1, and the infusion periods without such a magnitude of
force are counted as O.sub.i2. The counts (O) are respective
observed values. The total number M.sub.1 of historical data values
over a long cycle for which the magnitude of force was applied is
then computed:
M 1 = i = 1 I O i 1 ##EQU00001##
where I is the number of dose periods in a long cycle. M.sub.2 is
computed similarly, using the O.sub.i2 values. The numbers N.sub.i
of readings for each dose period i are then computed, as is the
total number of readings N in the long cycle:
N i = O i 1 + O i 2 ##EQU00002## N = i = 1 I N i ##EQU00002.2##
[0063] Expected values E.sub.ij are then computed:
E i 1 = N i M 1 N ##EQU00003##
for historical data for which the selected magnitude of force was
applied, and
E i 2 = N i M 2 N ##EQU00004##
for historical data for which the selected magnitude of force was
not applied. In these equations, N is the total number of samples,
e.g., the number of times insulin was delivered, in the long cycle.
N.sub.i is the total number of samples in each dose period. M.sub.1
is the total number of samples across the long cycle in which force
was applied beyond the threshold. The expected value E.sub.i1
represents the likely number of times that force would be applied
beyond the threshold if such force were equally likely to be
applied every time a dose was infused (every infusion cycle). Such
evenly-spread likelihood is referred to as an "equal
distribution."
[0064] The processor then computes a chi-squared (.chi..sup.2)
statistic using the O and E values as follows:
.chi. 2 = j = 1 2 i = 1 I ( O ij - E ij ) 2 E ij : ##EQU00005##
[0065] where j=1 refers to infusion periods with the magnitude of
force and j=2 refers to infusion periods without the magnitude of
force, and i is an index of the dose period in a long cycle. For
hourly dose periods in a day, i ranges from 1 to 1=24. This is an
example of Pearson's .chi..sup.2 test; other tests can also be
used.
[0066] The .chi..sup.2 value is compared to a .chi..sup.2
distribution with an appropriate number of degrees of freedom (DOF)
to determine significance. In the hourly/daily example, the number
of DOF is 23. The probability that the computed .chi..sup.2 value
corresponds to the equal distribution is determined from the
.chi..sup.2 distribution. If the resulting probability is less than
a selected confidence threshold (e.g., 0.05 or 0.01), the processor
286 determines that the long cycle includes dose period(s) that are
statistically different from other dose period(s) in the long
cycle. That is, at least one dose period during the tested long
cycle has deltas or deviations that are different from the deltas
for the other dose periods in a statistically significant way. If
the .chi..sup.2 test indicates that there is not such a
statistically significant difference between the observed values
and the equal distribution, the processor 286 can determine that
either the initial basal profile is correctly tracking the
patient's physiology, or that deviations (and force) are relatively
consistent across the long cycle.
[0067] If the .chi..sup.2 test indicates that at least some of the
deviations are not consistent across the long cycle (e.g.,
probability less than 0.05), the processor 286 further determines a
Z score for each dose period. The processor 286 then determines and
annunciates adjustments for dose periods with a Z-value having a
magnitude greater than a threshold, e.g., |Z|>2.0.
[0068] To compute the Z-score, the processor 286 first computes the
standard error SE.sub.i of each dose period i:
SE i = E i 1 ( 1 - 1 N ) ##EQU00006##
The processor 286 can then compute the Z-score for each dose period
i:
Z i = ( O i 1 - E i 1 ) SE i ##EQU00007##
The Z-value for time period i corresponds to the number of standard
deviations away from the mean dose period i is, but uses values
from a sample, not a statistical population.
[0069] Table 1 shows an exemplary .chi..sup.2 table for hourly dose
periods and daily long cycles. Table 1 shows how the various
quantities described above interrelate.
TABLE-US-00001 TABLE 1 Samples with Samples with Force Not Force
Applied Applied Obs Exp Obs Exp Row Total SE Z Hour 1 O.sub.11
E.sub.11 O.sub.12 E.sub.12 N.sub.1 SE.sub.1 Z.sub.1 Hour 2 O.sub.21
E.sub.21 O.sub.22 E.sub.22 N.sub.2 SE.sub.2 Z.sub.2 . . . . . . . .
. . . . . . . . . . . . . . . . Hour i O.sub.i1 E.sub.i1 O.sub.i2
E.sub.i2 N.sub.i SE.sub.i Z.sub.i . . . . . . . . . . . . . . . . .
. . . . . . . Hour I = 24 O.sub.I1 E.sub.I1 O.sub.I2 E.sub.I2
N.sub.I SE.sub.I Z.sub.I M.sub.1 M.sub.2 N
[0070] If the .chi..sup.2 test indicates that the deviations are
consistent across the long cycle (e.g., probability greater than
0.05), the processor 286 in at least one example determines a
single first basal-profile adjustment for at least two different
ones of the plurality of time periods. Since the deviations for the
time periods are consistent, the adjustments can also be
consistent.
[0071] In various embodiments, the processor 286 can be further
adapted to adjust the initial basal profile based upon the computed
first basal-profile adjustment(s). This adjustment can be done on a
selected time granularity, e.g., once per long cycle or per
selected number of long cycles; once per month; once per quarter;
or at other intervals. This can advantageously improve the accuracy
of insulin dosing for each specific patient 1138.
[0072] In various aspects, the user interface 230 includes a
display such as the touchscreen 144, FIG. 1. The processor 286 can
be configured to annunciate the computed first basal-profile
adjustment(s) by presenting a visual indication thereof on the
display. In an example, each first basal-profile adjustment
includes a respective delivery rate in U/hr. The visual indication
includes textual representation(s) of the respective delivery
rate(s).
[0073] In at least one embodiment, the user interface 230 is
adapted to receive input, e.g., from the patient 1138. The
processor 286 can be further adapted to receive the historical data
via the user interface 230 and store the received historical data
in the storage device 240. For example, the patient 1138 can record
pump levels on paper, and enter those into the controller 104 via
the user interface 230. The insulin delivery device 102 can also
store the historical data on a removable medium, e.g., a Flash
drive, and the user interface 230 can receive that removable medium
to be read by the processor 286.
[0074] The user interface 230 can include a mouse, a keyboard,
another computer (connected, e.g., via a network or a null-modem
cable), a microphone and speech processor or other device(s) for
receiving voice commands, a camera and image processor or other
device(s) for receiving visual commands, e.g., gestures, or any
device or combination of devices from which data is input to the
processor 286. In this regard, although the peripheral system 220
is shown separately from the user interface 230, the peripheral
system 220 can be included as part of the user interface 230. In at
least one embodiment, the user interface 230 can be operated by the
patient 1138.
[0075] In an example, the historical data includes bolus data. The
processor 286 can be further configured to filter meal data out of
the historical data using the bolus data. For example, before a
meal, the patient 1138 will often request a bolus to process the
estimated carbohydrate content of the meal. Some controllers 104 or
insulin delivery devices 102 permit the patient 1138 to indicate
that a particular bolus is a preprandial (pre-meal) bolus. These
indications are stored as part of the historical data. Accordingly,
the historical data for, e.g., 1.5 hours to four hours after the
meal bolus can be disregarded since glucose levels are not at the
steady state the basal profile is designed to maintain. Other
controllers 104 or insulin delivery devices 102 do not permit the
patient 1138 to provide such an indication. However, preprandial
boluses are often larger in mass or volume than correction boluses.
A threshold can be stored in the storage device 240, and the
processor 286 can compare bolus(es) indicated in the historical
data to the threshold and disregard data after over-threshold
boluses.
[0076] In various examples, the processor 286 is further configured
to store blood glucose measurements of the patient and filter meal
data out of the historical data using the stored measurements of
the blood glucose. The processor 286 can receive the blood glucose
measurements from the measurement device 200. In an example, the
measurement device 200 is a CGM sensor testing approximately every
five minutes. A hyperglycemic excursion lasting, e.g., more than 30
minutes, with an average increase in BG of 3 mg/dL/min, can
indicate the onset of blood sugar increase due to a meal.
Historical data following the meal, and optionally for a selected
time before the meal, can be disregarded.
[0077] In various aspects, the processor 286 is further configured
to store a plurality of the blood glucose measurements from the
measurement device 200. The processor 286 then determines
deviations of blood glucose level from a stored aim range for one
or more dose period(s) using the stored blood glucose measurements.
The dose period(s) used with respect to the historical data can be
the same as, or different from, the does period(s) used with
respect to the blood glucose measurements. The processor 286
computes a respective second basal-profile adjustment for each of
the one or more dose period(s) using the determined deviations. For
some dose period(s), the processor 286 can provide a second
basal-profile adjustment of zero (i.e., unchanged). The processor
286 then annunciates at least some of the respective second
basal-profile adjustment(s), e.g., via the user interface 230. The
processor 286 can annunciate by presenting via the user interface
230 a human-perceptible indication of the second basal-profile
adjustments.
[0078] The stored blood glucose measurements can cover, e.g., 30
days (or long cycles) with 1-2 glucose measurements per day (long
cycle), or up to 90 days, or as few as 14 days. In an aspect,
shorter coverage periods include more glucose measurements per long
cycle, e.g., three tests for each of 14 days. Glucose measurements
can be spread out over the course of a long cycle.
[0079] In an example, the deviation is the average extent (in
mg/dL) by which blood glucose measurements are out of an aim
glucose range over the dose period. The second basal-profile
adjustment for a given dose period can be (in U/hr) the deviation
for that dose period divided by the insulin sensitivity factor
(ISF) for that dose period. In another example, the deviation is
the difference between the target glucose and the average glucose
for all readings during the dose period, over the course of, e.g.,
30 days. The adjustment is that difference divided by ISF. The
adjustment can be applied ahead of the time of the glucose
measurement, e.g., one hour ahead. For example, the long cycle can
be daily and the dose period can be hourly (numbered from 0 to 23).
For a glucose measurement in dose period p, the adjustment can be
applied to dose period p-1. If a glucose excursion extends over
more than one dose period, the processor 286 can determine
adjustments to more than one dose period.
[0080] In various aspects, the processor 286 is configured to
determine the deviations of blood glucose level by determining the
extent to which each of the stored blood glucose measurements is
outside the stored aim range, as described above. The processor 286
is further configured to determine that the deviation for one of
the time period(s) is zero if the stored measurements during that
time period are within the stored aim range. This advantageously
permits the basal rate to continue unadjusted as long as blood
glucose is experiencing normal variation within the aim range, and
reduces the effects of natural noise on the computation of the
second basal-profile adjustments. The deviations can be positive or
negative, and the corresponding adjustments can be negative or
positive. This can advantageously assist in reducing the incidence
of hyperglycemia or hypoglycemia, or providing a doctor information
to do so.
[0081] The processor 286 can be further configured to filter meal
data out of the stored blood glucose measurements using the
historical data. For example, meals can be located using the
historical data as described above. Examples include using bolus
data; using data entered in a food tracking database, e.g., on an
insulin pump or on the controller 104; and receiving input from the
user's paper records or from a diabetes management system. Stored
blood glucose measurements within the 1.5-4 hours after a meal can
then be disregarded, or adjusted downward by, e.g., 50 mg/dL, to
correct for the effects of ingested carbohydrates.
[0082] In various embodiments for computing the second
basal-profile adjustments, the processor 286 is configured to store
and use ISF values in its calculations. In general, parameters such
as ISF and I:C can be used for various purposes. Various aspects
described herein therefore provide devices and methods for
annunciating adjustments to the parameters. ISF can be used by a
bolus calculator in determining a bolus amount to move a patient's
blood sugar from out-of-range to target. Such calculators can be
particularly useful to people who test more frequently, e.g.,
patients with Type 1 diabetes who test at least three times per
day.
[0083] In various aspects, the processor 286 is further configured
to store a plurality of the blood glucose measurements for a
selected time period, e.g., a full day or other long cycle, or a
dose period. In an example in which the time period is a full day,
if the patient takes glucose measurements using an episodic meter
at 6 am, 2 pm, and 10 pm, the processor 286 can store the 14 (or
more) 6 am measurements, the .gtoreq.14 2 pm measurements, and the
.gtoreq.14 10 pm measurements. In an example using CGM data and a
one-hour time period, the processor stores 12 glucose measurements
per hour (at five-minute intervals) and stores up to all 12
measurements for each hour of the day for 14 days.
[0084] The processor 286 is configured to select two stored
measurements using the historical data, the two stored measurements
corresponding to a glucose correction bolus during the selected
time period. The two measurements include a "before" measurement
and an "after" measurement. The "before" measurement can be the
most recent BG measurement before the bolus or other BG measurement
used in computing the bolus amount. The "after" measurement can be
a BG measurement taken between, e.g., 1.5 and 4 hours after the
bolus, or a median or average of multiple BG measurements between
those times. Glucose correction boluses can be located in the
historical data as described above. The processor 286 can also
select multiple pairs of "before" and "after" measurements and
perform processing described below for each pair. In various
examples, the processor 286 selects and uses 20 pairs, or at least
20 pairs, of measurements in the given time period.
[0085] The processor 286 is further configured to determine a
glucose effect of the glucose correction bolus using the selected
stored measurements and a stored aim range. This can be done by
adjusting the "after" value to be equal to a selected target if the
"after" measurement is within the selected aim range. The (possibly
adjusted) "after" value is then subtracted from the "before" value
to determine the drop in insulin resulting from the bolus. This
drop is the glucose effect (.DELTA.mg/dL). In various aspects using
multiple pairs of "before" and "after" measurements, the processor
286 computes a respective glucose effect for each pair of
measurements. In an example, the American Diabetes Association
recommends preprandial glucose of 90-130 mg/dL. The selected target
can thus be the midpoint of that range, 110 mg/dL. The "before" and
"after" values can be in the normal, hypoglycemic, or hyperglycemic
ranges.
[0086] The processor 286 is still further configured to compute an
adjustment to an insulin sensitivity factor (ISF) for the selected
time period using the determined glucose effect, and to annunciate
the computed adjustment to the insulin sensitivity factor. The
annunciating can be via the user interface 230. As above, there can
be time periods for which an adjustment is not computed or not
annunciated, e.g., periods for which the adjustment is smaller than
the resolution of the ISF value stored in the insulin delivery
device 102 (e.g., 1.0 U/mg/dL). The processor 286 can compute the
adjustment by dividing the size of the bolus (U) by the determined
glucose effect (.DELTA.mg/dL). ISF can be expressed as
U/(.DELTA.mg/dL) or as (.DELTA.mg/dL)/U; either can be computed by
the processor 286. In various aspects using multiple pairs of
"before" and "after" measurements, the processor 286 computes a
respective adjustment for each pair of measurements, then averages
the respective adjustments to determine the adjustment to the ISF.
This averaging can mitigate the effect on ISF of user errors in
manual data recording or data entry.
[0087] In various aspects, the processor 286 is further configured
to automatically apply the computed adjustment to the stored
insulin sensitivity factor(s) corresponding to the time period. The
ISF can be updated for the time at which the bolus was
delivered.
[0088] Another parameter is insulin-carbohydrate ratio (I:C), which
can be stored in the storage device 240. The patient, via the
controller 104, can use I:C ratio to compute the appropriate amount
of insulin for a pre- or postprandial bolus. In various aspects,
the processor 286 is configured to annunciate an adjustment to I:C.
The processor 286 is configured to store a plurality of the blood
glucose measurements for a selected time period, as discussed
above. The processor 286 is further configured to select a stored
measurement using the historical data, the selected measurement
corresponding to a carbohydrate correction bolus during the
selected time period. For example, the selected measurement can be
a measurement taken between 1.5 and 4 hours after such a bolus.
[0089] The processor 286 is configured to determine a respective
deviation for each of the selected stored measurements with respect
to a stored aim range. In an example, if the respective measurement
of glucose is within the aim range, that measurement is adjusted to
be equal to the target. Each deviation is then determined as
(adjusted) measurement minus target BG.
[0090] In an example, the storage device holds a
glucose-carbohydrate ("G:C") ratio. The G:C ratio represents the
typical effect on blood glucose of ingesting a unit amount of
carbohydrate. In an example, the G:C ratio is 5 mg/dL per gram CHO.
The G:C ratio can be obtained from clinical studies and can be
varied per patient. Typical G:C ratios are between 5 and 10
mg/dL/g(CHO), but ratios outside that range can also be used.
[0091] In this example, the processor 286 computes an adjustment to
the insulin-carbohydrate ratio for the selected time period using
the determined deviations and the insulin-carbohydrate ratio, and
the G:C ratio. The processor 286 then annunciates, e.g., via the
user interface 230, the respective adjustment to the
insulin-carbohydrate ratio. The processor 286 can compute an
adjustment for all or fewer than all time periods in, e.g., a long
cycle or other time range, and the time period(s) can be the same
as those used in determining adjustments for basal rates and ISF,
or can be different. In this example, the adjustment can be
computed by taking the average of all the deviations and dividing
by the G:C ratio. In various aspects, adjustments can be computed
only if the average deviation has more than a selected magnitude,
e.g., 30 mg/dL.
[0092] In various aspects, the processor 286 is further configured
to automatically update the I:C ratio stored in the storage device
240.
[0093] The processor 286 includes one or more data processor(s)
that implement processes of various embodiments described herein,
e.g., embodiments discussed above and methods shown in FIGS. 3A-3B,
discussed below. A "data processor" is a device for processing data
and can include a central processing unit (CPU), a desktop
computer, a laptop computer, a mainframe computer, a personal
digital assistant, a digital camera, a cellular phone, a
smartphone, or any other device for processing data, managing data,
or handling data, whether implemented with electrical, magnetic,
optical, biological components, or otherwise. The phrase
"communicatively connected" includes any type of connection, wired
or wireless, between devices, data processors, or programs in which
data can be communicated. Subsystems such as the peripheral system
220, the user interface 230, and the storage device 240 are shown
separately from the processor 286 but can be stored completely or
partially within the processor 286.
[0094] The storage device 240 includes or is communicatively
connected with one or more tangible non-transitory
computer-readable storage medium(s) configured to store
information, including the information needed to execute processes
according to various embodiments. The term "device" does not imply
that storage device 240 include only one piece of hardware that
stores data. A "tangible non-transitory computer-readable storage
medium" as used herein refers to any non-transitory device or
article of manufacture that participates in storing instructions
which may be provided to the processor 286 for execution. Such a
non-transitory medium can be non-volatile or volatile. Examples of
non-volatile media include floppy disks, flexible disks, or other
portable computer diskettes, hard disks, magnetic tape or other
magnetic media, Compact Discs and compact-disc read-only memory
(CD-ROM), DVDs, BLU-RAY disks, HD-DVD disks, other optical storage
media, Flash memories, read-only memories (ROM), and erasable
programmable read-only memories (EPROM or EEPROM). Examples of
volatile media include dynamic memory, such as registers and random
access memories (RAM).
[0095] Computer program instructions are read into the memory 241
from the disk 242, or a wireless, wired, optical fiber, or other
connection. The processor 286 then executes one or more sequences
of the computer program instructions loaded into the memory 241, as
a result performing process steps and other processing described
herein. In this way, the processor 286 carries out a computer
implemented process that provides technical effects described
herein. For example, blocks of the flowchart illustrations or block
diagrams herein, and combinations of those, can be implemented by
computer program instructions.
[0096] In various embodiments, the processor 286 is communicatively
connected to a communication interface 215 that is coupled via a
network link 216 to the network 116. For example, the communication
interface 215 can be a WIFI or BLUETOOTH SMART wireless transceiver
and the network link 216 can be a radio-frequency (RF)
communications channel. As another example, the communication
interface 215 can be a network card to provide a data communication
connection to a compatible local-area network (LAN), e.g., an
Ethernet LAN, or wide-area network (WAN). The communication
interface 215 sends and receives electrical, electromagnetic or
optical signals that carry digital data streams representing
various types of information across the network link 216 to the
network 116. The network link 216 can be connected to the network
116 via a switch, gateway, hub, router, or other networking
device.
[0097] The processor 286 can send messages and receive data,
including program code, to and from the network 116 via the network
link 216 and the communication interface 215. For example,
requested code for an application program (e.g., a JAVA applet or
smartphone app) can be stored on a tangible non-volatile
computer-readable storage medium connected to the network 116. A
network server (not shown) can retrieve the code from the medium
and transmit it via the network 116 to the communication interface
215. The received code can be executed by the processor 286 as it
is received, or stored in the storage device 240 for later
execution.
[0098] Moreover, program code to carry out methods described herein
can execute entirely on a single processor 286 or on multiple
communicatively-connected processors 286. For example, code can
execute wholly or partly on a user's computer and wholly or partly
on a remote computer, e.g., a server. The remote computer can be
connected to the user's computer through the network 116. The
user's computer or the remote computer can be non-portable
computers, such as conventional desktop personal computers (PCs),
or can be portable computers such as tablets, cellular telephones,
smartphones, or laptops.
[0099] Embodiments of the present invention can take the form of
computer program products embodied in one or more tangible
non-transitory computer readable medium(s) having computer readable
program code embodied thereon. Such medium(s) can be manufactured
as is conventional for such articles, e.g., by pressing a CD-ROM.
The program(s) embodied in the medium(s) include computer program
instructions that can direct the processor 286 to perform a
particular series of operational steps when loaded, thereby
implementing functions or acts specified herein.
[0100] FIGS. 3A-3B are a flowchart illustrating exemplary methods
for recommending adjustments. For example, illustrated is a method
for recommending a basal-rate adjustment for an insulin-delivery
system. For clarity of explanation, reference is herein made to
various components shown in FIGS. 1 and 2 that can carry out or
participate in the steps of the exemplary method. Accordingly, the
method can include automatically performing steps described herein
using the processor 286, FIG. 2. It should be noted, however, that
other components can be used; that is, the exemplary method is not
limited to being carried out by the identified components. For
purposes of this exemplary embodiment, processing begins with step
305.
[0101] In step 305, a physiological parameter of a patient is
continuously measured. As described above, "continuous" measurement
can be recurring, e.g., every 5 minutes. The physiological
parameter can be, e.g., blood glucose. Step 305 can be followed by
step 310 or step 335.
[0102] In step 310, the patient is infused with insulin according
to an initial basal profile and the continuous physiological
parameter measurements.
[0103] In step 315, historical data of the delivery of insulin are
stored. The next step can be step 340 or step 310. In this way, the
patient is repeatedly infused with insulin. Steps 310, 320, and 330
can be repeated in any order or combination, and any number of
times.
[0104] In step 320, using the processor 286, deviations of the
delivery of insulin from the basal profile are automatically
determined for one or more time period(s) using the stored
historical data. This can be done as described above with reference
to FIG. 2.
[0105] In step 325, using the processor, a respective first
basal-profile adjustment is automatically computed for each of the
time period(s) using the determined deviations. This can be done as
described above with reference to FIG. 2.
[0106] In step 330, using the processor, the computed first
basal-profile adjustment(s) is/are automatically annunciates, e.g.,
via the user interface 230. This can be done as described above
with reference to FIG. 2.
[0107] In various aspects, the measurements taken in step 310 are
provided to step 335. In step 335, using the processor, a plurality
of the blood glucose measurements is stored. This can be done as
described above with reference to the storage device 240, FIG. 2.
Step 335 can be followed by step 355 or step 375.
[0108] In step 340, using the processor 286, deviations of blood
glucose level from a stored aim range for one or more time
period(s) are determined using the stored measurements. This can be
done as described above with reference to FIG. 2. As discussed
above, the time periods used for glucose-data processing can be
different from those used for historical-data processing.
[0109] In step 345, respective second basal-profile adjustment(s)
are computed for at least some of the time period(s) using the
determined deviations. This can be done as described above with
reference to FIG. 2. For example, steps 325 or 345 can include
performing i or other statistical tests, as described above, as can
steps 365 and 385, discussed below.
[0110] In step 350, at least some of the computed second
basal-profile adjustment(s) is/are annunciated. This can be done as
described above with reference to the user interface 230, FIG.
2.
[0111] Referring to FIG. 3B, step 355 can follow step 335, FIG. 3A.
After the blood glucose measurements for a selected time period are
stored in step 335, two stored measurements are selected using the
historical data. The two selected measurements correspond to a
glucose correction bolus during the selected time period. The
measurements can be, e.g., before-bolus and after-bolus
measurements. This selection can be done as described above with
reference to FIG. 2.
[0112] In step 360, using the processor 286, a glucose effect of
the glucose correction bolus is determined using the selected
stored measurements and a stored aim range. This can be done as
described above with reference to FIG. 2.
[0113] In step 365, an adjustment to an insulin sensitivity factor
is computed for the selected time period using the determined
glucose effect. This can be done as described above with reference
to FIG. 2.
[0114] In step 370, the computed adjustment to the insulin
sensitivity factor is annunciated. This can be done as described
above with reference to the user interface 230, FIG. 2.
[0115] Step 375 can follow step 335, FIG. 3A. After the blood
glucose measurements for a selected time period are stored in step
335, at least one of the stored measurements is selected by the
processor 286 using the historical data. The at least one selected
measurement corresponds to a carbohydrate correction bolus during
the selected time period. This can be done as described above with
reference to FIG. 2.
[0116] In step 380, a respective deviation for each selected stored
measurement is automatically determined with respect to a stored
aim range. This can be done as described above with reference to
FIG. 2.
[0117] In step 385, an adjustment to the insulin-carbohydrate (I:C)
ratio is computed for the selected time period using the determined
deviations and the insulin-carbohydrate ratio. This can be done as
described above with reference to FIG. 2.
[0118] In step 390, at least some of the computed adjustment(s) to
the insulin-carbohydrate ratio is/are annunciated. This can be done
as described above with reference to the user interface 230, FIG.
2.
[0119] In various aspects, in step 333, a determined adjustment is
automatically applied. The adjustment can be an adjustment
determined in any of steps 330, 350, 370, or 390. This can be done
as described above with reference to FIG. 2, e.g., by updating data
of the basal profile, ISF, or I:C in the storage device 240.
[0120] In a first aspect of a method for recommending a basal-rate
adjustment for an insulin-delivery system, steps 305, 310, 315,
320, 325, and 330 are performed in that order. In a second aspect
of a method for recommending a basal-rate adjustment for an
insulin-delivery system, steps 305, 335, 340, 345, 350 are
performed in that order. In a third aspect of a method for
recommending an ISF adjustment for an insulin-delivery system,
steps 305, 335, 355, 360, 365, 370 are performed in that order. In
a fourth aspect of a method for recommending an I:C adjustment for
an insulin-delivery system, steps 305, 335, 375, 380, 385, 390 are
performed in that order. In various aspects, one or more of the
first through fourth aspects are performed in any combination and
in any order. The processor 286 can carry out computation steps of
various of the first through fourth aspects interleaved in time, or
sequentially. In this way, the first through fourth aspects can
each be used independently, or can be used in any combination.
[0121] In view of the foregoing, embodiments of the invention
provide improved management of data relevant to basal rates and
parameters. A technical effect of processing performed by the
processor 286 is to compute adjustment recommendations using data
provided, e.g., by the measurement device 200, and to compute
graphical representations of those recommendations. A further
technical effect is to present the graphical representations
outside the particular computing device that performed the
computations, e.g., to the patient or a healthcare provider who may
use the recommendations in determining basal rates or parameters. A
further technical effect of various embodiments is to automatically
adjust basal rates or parameters to improve control of the
patient's blood glucose by the insulin delivery device 102 and the
controller 104. Various decision-support systems and devices
described herein can be integrated with, e.g., episodic blood
glucose meters or drug-delivery devices. Various methods described
herein can be performed by processors in such meters or
devices.
PARTS LIST FOR FIGS. 1-3B
[0122] 100 insulin delivery system [0123] 102 insulin delivery
device [0124] 104 controller [0125] 106 infusion set [0126] 108
flexible tubing [0127] 110 radio frequency (RF) communications link
[0128] 111 radio frequency (RF) communications link [0129] 112
continuous glucose monitoring (CGM) sensor [0130] 113 radio
frequency (RF) communications link [0131] 114 glucose meter [0132]
115 test strip [0133] 116 network [0134] 117 radio frequency (RF)
communications link [0135] 118 radio frequency communications link
[0136] 125 test strip [0137] 130 housing [0138] 144 touchscreen
[0139] 145 exemplary tape indicator [0140] 146 soft key [0141] 200
measurement device [0142] 215 communication interface [0143] 216
network link [0144] 220 peripheral system [0145] 230 user interface
[0146] 240 storage device [0147] 241 memory [0148] 242 disk [0149]
286 processor [0150] 305, 310, 315, 320, 325 steps [0151] 330, 335,
340, 345, 350 steps [0152] 355, 360, 365, 370, 375 steps [0153]
380, 385, 390 steps [0154] 1138 patient
[0155] While the invention has been described in terms of
particular variations and illustrative figures, those of ordinary
skill in the art will recognize that the invention is not limited
to the variations or figures described. In addition, where methods
and steps described above indicate certain events occurring in
certain order, those of ordinary skill in the art will recognize
that the ordering of certain steps may be modified and that such
modifications are in accordance with the variations of the
invention. Additionally, certain of the steps may be performed
concurrently in a parallel process when possible, as well as
performed sequentially as described above. Separate references to
"an embodiment" or "particular embodiments" or the like do not
necessarily refer to the same embodiment or embodiments; however,
such embodiments are not mutually exclusive, unless so indicated or
as are readily apparent to one of skill in the art. The use of
singular or plural in referring to "method" or "methods" and the
like is not limiting. The word "or" is used in this disclosure in a
non-exclusive sense, unless otherwise explicitly noted. To the
extent there are variations of the invention that are within the
spirit of the disclosure or are equivalent to the inventions found
in the claims, it is the intent that this patent will cover those
variations as well.
* * * * *